The influence of emergency department responsiveness on throughput times.

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The influence of emergency department

responsiveness on throughput times.

Annemiek Koomen University of Groningen Faculty of Economics and Business

MSc: Supply Chain Management

24 January 2016

Supervisors: Dr. J.T. van der Vaart

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Reducing throughput times for patients at emergency departments (ED) has become really important. Nowadays, there are more crowded EDs, longer patients’ length of stays and therefore longer waiting times for patients (Van der Linden et al., 2013). The reason is an increase in patient demand, the closure of EDs and the reduction in budgets which put pressure on the capacity of emergency departments (Solberg, Asplin, Weinick & Magid, 2003). The capacity should always be sufficient in a way that patients in urgent need of treatments can get medical help at any time (Van der Vaart, Vastag & Wijngaard, 2011). However, the possibilities for increasing capacity are limited because of the reduction in budgets. The variability in arrivals, volume and needed amount of resources make the planning of capacity even more complex. One of the possibilities to decrease throughput times could be responsiveness (Van Achteren, 2014). During this research the influence of responsiveness with capacity on the throughput times will be discussed.

When the capacity is not able to handle the input of patients, crowding occurs (Asplin, Magid, Rhodes, Solberg, Lurie & Camargo, 2003). Overcrowding is ‘a situation in which demand for service exceeds the ability to provide care within a reasonable time, causing physicians and nurses to feel too rushed to provide quality care’ (Derlet & Richards, 2000). Crowding negatively affects throughput times (McCarthy et al., 2009) which is the time from patient arrival to the time of discharge (Chan, Reilly & Salluzzo, 1997). There are more factors influencing throughput times negatively, for example, the arrival variability, treatment variability and availability of staff (Ekelund et al., 2011; Van der Vaart et al., 2011). These factors cause difficulties regarding planning of capacity. When the inflow of patients increases, capacity is not able to handle all patients, queuing will increase, which results in longer throughput times and waiting times (Hopp & Spearman, 2008).

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5 responsiveness is important. Regarding the variable environment of the emergency department, flexible capacity could be helpful. According to Van Achteren (2014) the emergency department does not response adequate in the morning when more patients arrive.

No research has been done to understand the influence of responsiveness on throughput times and waiting times for patients. An increase of responsiveness will lead to more productivity which improves the speed of the processes of patients. During this research an intervention will be conducted, during mornings employees are motivated to response quicker. This probably leads to a decrease in throughput times for patients, which enables the possibility to prove that responsiveness really influences throughput times. This leads to the following research question:

What is the influence of responsiveness on throughput times for patients at the emergency department?

This question will be researched in a case study conducted within the emergency department of the Medical Centre Leeuwarden (MCL). Quantitative data from the department will be used to analyze throughput times. The research will be completed by qualitative data in the form of observations and interviews.

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3. Theoretical framework

In this paper the influence of responsiveness on throughput times for patients at emergency departments will be researched. In this theoretical section the relevant variables will be explained on the basis of extent literature.

3.1. Emergency departments The process

Patients arriving at the emergency department can arrive by ambulance or by themselves (Lane, Monefeldt & Rosenhead, 2000). Furthermore they can arrive by helicopter or by referral of a general practitioner, always without prior appointments. In cases of life-threatening situations patients will be treated immediately. The non-critical patients will be registered by the arrival desk and mostly have to wait in the waiting room. To determine how critical the diseases of patients are, and how quickly they should be treated the Manchester triage system has been developed. A specific nurse determines the criticality of patients, they receive a certain color. Different colors explain in what amount of time a patient should be helped to maintain the patient safe. The different levels of urgency are ‘emergent’ (red), immediately evaluation is needed, ‘very urgent’ (orange), within 10 minutes evaluation is needed, ‘urgent’ (yellow), within 60 minutes evaluation is needed, ‘standard’ (green), within 120 minutes evaluation is needed and ‘non-urgent’ (blue), these patient can wait up to 240 minutes before evaluation is needed (Roukema, Steyerberg, Van Meurs, Ruige, Van der Lei & Moll, 2006). Depending on their level of urgency patients are sent back into the waiting room or will be treated immediately. During the triage or during the start of the treatment patients get assigned to a specific specialism. An appropriate specialist or assistant of the specialist will take care of the patients of their specialism. The patients also get assigned to a specific nurse, the patient will be treated only by their assigned specialist or assistant and nurse. After the treatment patients leave the ED differently, some patients are able to go home, while others will be admitted. Depending on their situation they go to the intensive care department, the operating room or a nursing ward within the hospital.

staff

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7 co-assistants and management are working at the ED. However, depending on the moment of the day or the week it differs when these are presented.

3.2. Overcrowding

The last few years, there are multiple changes in emergency departments. More patients arrive, there are nursing shortages, administrative and specialty staff shortages and there is lack of room availability (Derlet & Richards, 2000). In the Netherlands this is not yet a major problem, however there is evidence that patients at the emergency department nowadays experience longer length of stay, which indicate crowding (Van der Linden et al., 2013). The effects of overcrowding can lead to serious problems, such as prolonged pain and suffering, public safety at risk and a lot of dissatisfaction of patients (Derlet & Richards, 2000). Also the pressure rises for physicians and nurses, they have to deal with a lot of stress and are most of the time in a hurry. This increasing pressure could affect productivity and quality on a long term (Derlet & Richards, 2000). Solutions should be found to overcome these serious problems, however, this can be difficult regarding the multiple plans to decrease healthcare costs at emergency departments (Van der Linden et al., 2013). Reducing throughput times is probably one of the options to solve this problem.

3.3. Factors influencing throughput times

The focus in this research is the throughput times of patients at the emergency department. There are multiple factors which influence throughput times, for example an increase of patients arrivals, day of the week, availability of staff, duration of stay, severity of trauma, unscheduled ambulatory care, the inpatient medicine occupancy rate and the increase in administration (Ekelund et al., 2011; Van der Vaart et al., 2011; Yen & Gorelick, 2007; Asplin et al., 2003; McCarthy et al., 2009).

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8 treatments and resources. Furthermore, the variability in staff influences the throughput times, the experiences and work methods differ between physicians and nurses. The different patterns physicians follow account for a lot variation (Smith, Martin, Langefeld, Miller & Freedman, 1995). Besides the highly unpredictable arrival variability, also the treatment variability and variability of staff leads to the complexity of the ED (Lane et al., 2000) and all these different variabilities influence throughput times of patients.

3.4. Capacity

Patients arriving at emergency departments can be critical or non- critical. The critical patients are in immediate need for healthcare (Lane et al., 2000). The ED should always be available for receiving patients in urgent need of treatment at any time (Van der Vaart et al., 2011). Therefore the timely access to the ED is important, this determines the quality of ED’s. To enable timely access to the ED, the capacity of resources should be sufficient. However, forecasting the necessary capacity can be difficult regarding the multiple aspects of variability of patients. It is mostly unpredictable how much and which specific capacity is needed at different moments of the day. However, most important is that capacity is always sufficient so that patients arriving with life-threatening illnesses can be treated soon.

Capacity management

To maintain patient safety and improve quality of healthcare, capacity management is a critical component (McCaughey, Erwin, DelliFraine & McVey, 2015). Demand is unpredictable in healthcare and capacity should be sufficient, otherwise there can be consequences for patients as well as employees and the whole organization (McCaughey et al., 2015). Hospitals determine a certain time-limit within the patient should be treated. Firstly, a time-limit exist before a patient should be triaged and assigned to a certain code. The code of triage determines the time within the treatment should be started. The capacity is sufficient when all patients are treated within that certain time-limit.

Resources

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9 space or personnel (Dijk, 2013; Kellermann, 2006; Yen & Gorelick, 2007). There should be enough treatment rooms and enough required personnel to make sure every patient will receive care in time. The focus in this research will be on the staff of the emergency department. According to McCaughey et al. (2015) mainly the availability of staff is the factor which causes delays in the process. Therefore, improvements are possible for this type of resources within the ED (Van Achteren, 2014). Earlier research found some indications that the staff of the ED does not always work efficient, especially during the mornings. This leads to opportunities to increase productivity of staff (Van Achteren, 2014).

To decrease throughput times, improvements are possible within the capacity of resources at the emergency department. In situations were high variability exist there are three options to deal with this; keep products in stock, let products wait and/or use more capacity. For the emergency department it is not possible to stock patients, and the focus for this research is to reduce throughput times. Therefore, an increase in waiting time is not an option and the solutions should be found at using more capacity. This can either be done by using more resources, or by increasing productivity of existing resources.

Trade-off

A trade-off exists between utilization and capacity of EDs. On the one hand hospitals might choose to employ more capacity to enable treatment of patients within a certain time-limit. On the other hand financial pressures results in a necessary increase of the utilization of capacity to increase efficiency. According to Pines et al. (2011) it is not clear whether more capacity or more efficiency will result in more revenue. Using more capacity leads to an increase in costs and more patients can be treated. Less patients will leave without being seen which results in more revenues for the ED. On the other hand more efficiency of the resources will save costs, however, less patients will be treated. Patients will leave without being seen which results in losses of potential revenues. The tradeoff is balancing the capacity and efficiency to receive the highest revenue, while ensuring the urgent patient can be helped any time.

3.5. Responsiveness

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10 detecting the moment the inflow of patients starts to increase and more patients should be treated is important. Responding on an increase in arrivals should lead to an immediate reaction of the staff to work faster, resulting in an increase of productivity.

More arrivals lead to more work in progress and therefore more queues and longer waiting times (Hopp & Spearman, 2008). Preliminary research recognized patterns of the inflow of patients, they mostly increase during the day and there are certain peak moments (Van Achteren, 2014). The emergency department responds by adding more capacity in the form of staff. Depending on the arrival pattern they deploy staff in time, however there are improvements possible to increase productivity of the available capacity. During mornings staff tend to respond late when the arrival of patients starts to increase (Van Achteren, 2014). When the ED starts increasing responsiveness in the morning, productivity will increase. This results in more optimal use of capacity which will prevent crowding of patients. Treatment rooms and staff will be earlier available for new patients arriving which decreases waiting times and throughput times.

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4. Methodology

The aim for this research is to gain insight in the effect of responsiveness on throughput times of patients at emergency departments. In order to see what these effects are, a single case study will be conducted. This is a form of research in a natural setting were different methods are used for data collection by a direct observer (Meredith, 1998). The research will investigate a new situation and it will be highly qualitative, therefore a single case study is appropriate (Meredith, 1998). Advantages of a single case study are the in-depth information and detailed explanation, a disadvantage is the ability of generalization (Karlsson, 2009). In the situation when multiple cases demonstrate a certain outcome they aid in generalization, the reliability of this outcome increased. For single case studies this is limited, however, an in-depth single case study can be generalized as well when the situation is applicable in other similar situations (Meredith, 1998). For example, when the research will be conducted at an ED with multiple similarities, you can assume the outcome will be the same.

4.1 MCL

The research will be done at the emergency department of the Medical Center Leeuwarden (MCL), a large hospital in the north of the Netherlands. During 2014 the hospital had 3224 employees, 690 beds and 28,969 hospital admissions a year (MCL, 2014). Furthermore, the MCL is a learning hospital, meaning that there are not only emergency physicians and specialists, but also assistants presented at the emergency department. These all are under supervision of a specialist or emergency physician.

4.2 Data Collection

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12 Furthermore, information about staff resources is collected, including the number and the types of employees as well as the staff schedules at the emergency department. To ensure data reliability, direct observation was undertaken. Direct observation of a first source is required to discover the ‘why’ and ‘how’ of situations (Meredith, 1998). Various employees at the emergency department have been observed. The daily tasks and routines of physicians, triage nurse, secretary and nurses have been observed. During the observation the moments of registration were checked, by which we can assess the reliability of the data. There were also some mistakes in the dataset, and some of the data was missing. Other data appeared to be inaccurate and corrections were therefore made. To correct mistakes and to retrieve missing information, the medical files of patients were consulted. By analyzing the medical files more detailed information concerning patients’ treatments was gathered, which enabled data correction.

4.3 Data analysis

Observation was also used to gain insight in the hospital and the tasks and routines of the staff at the ED. The dataset was then corrected and analyzed with Excel.

Data has been used to make throughput diagrams in order to discover whether the same patterns could be found as during the earlier research from Van Achteren (2014). In the operations management field a framework has been designed in which delivery, reliability and performance will be diagnosed. Make-to-order companies use this type of framework to improve throughput times (Soepenberg, Land & Gaalman, 2012). There are multiple similarities with the ED; make-to-order companies begin processing only when demand is made. This is also the case for emergency departments. The framework gave a clear overview of the arrivals and throughput times of the patients. Patient arrival patterns were analyzed in combination with the structural capacity planning of the emergency department. With this information the capacity availability was clarified.

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13 the end of a patient’s treatment staff were motivated to increase their working efficiency. During a week an alert was displayed at the central board of the MCL when a patient remained longer than the predetermined length of stay. This should increase the responsiveness of staff, as they were reminded to hasten this patient’s treatment.

Firstly a standard treatment time for patients has been determined, and within this time limit a patient should be treated totally and be ready to leave the emergency department. Determination of this two-hour time limit was made in collaboration with an emergency physician. It is an achievable time for physicians, and also allows for possible improvements. After two hours from the start of the treatment an alert was seen. At the central board of the emergency department bright pink ‘post-its’ were used for the appropriate patients. This reminded the staff that they had exceeded the time limit, prompting them to work faster. During the intervention more information about why patients required longer than two hours was recorded. When the two hours had passed the nurse, ZoCo (Care coordinator) or physician was asked for the cause of the delay. In addition to the ‘post-its’, a small conversation concerning patient stays would also prompt a hastening of the treatment process.

It is difficult to show the effects of the intervention merely by analyzing data. Analyzing the throughput times of the week of the intervention does not give generalizable results. The intervention lasted for one week and the high variability of the emergency department leads to data which is not generalizable. However, the effect of the intervention was noticed by multiple employees at the ED during this period. It was intended that the staff experienced the feeling to work faster to ensure patients were helped within the time limit. When the interviewees mentioned they were really influenced by the intervention and they got the feeling patients should be treated faster, this was an indication that the intervention really influenced throughput times.

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5. Results

In this chapter the results will be presented. Firstly, patient arrivals, throughput times and waiting times will be analyzed. Following this the results from the intervention will be analyzed.

5.1. Data analysis

In this part general information concerning arrivals and throughput times will be given, followed by the availability of the capacity of the emergency department. Next, the results from multiple analyses of data will be shown. The analyses may lead to the possibility for improving the process.

General information

In total there were 12499 patients arriving during the six months of the analysis, with an average of 68 patients per day. This number of arrivals is highly variable; the lowest number of patients on a single day was 45, while the highest was 100. Throughput times also showed high variability. The minimum was 1 minute while the maximum was 11 hours and 5 minutes. The median of the throughput time is 2 hours and 9 minutes.

Available capacity

At the emergency department there are multiple types of resources that can influence throughput times. These comprise the numbers of nurses, the numbers of physicians and the numbers of available treatment rooms, which will be discussed below.

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16 During weekdays there are four types of physicians available; emergency physicians, internal assistants, surgery assistants and/or co-assistants. From 8:00h two emergency physicians begin their shift, and at 15:00h another emergency physician shift starts. The emergency physicians have specific roles. One has a coordinating role, while the others have major roles in patient care. The coordinator focusses more on assigning physicians to patients, while the other physicians concentrate on treatments. The MCL is a learning hospital, and therefore assistants work under the supervision of a specialist. Sometimes emergency assistants are on duty, but this is not always the case. Furthermore, there is a permanent surgery assistant present at the emergency department. The first assistants start at 8:00h, at 17:00h a handover occurs, then from 22:00h the night shift starts. The internal assistants start at 8:00h, 13:00h and 17:00h. Therefore some overlap exists for these assistants. It is important that the available assistants are consistent with the number of patients in need of their specialism. Thus an analysis of the number of arriving patients assigned to specific specializations has been made. A table with information about patients assigned to a specific specialization can be found in Appendix A. Most patients are assigned to surgery, followed by cardiology and internal. A surgery and internal assistant is almost always present, while cardiology patients will be treated by both the internal assistant and the emergency physician. Thus most patients will be treated by a specialist who is almost always present at the emergency department. However, from the interview it became clear that the capacity of the physicians is not always sufficient. This can be caused by an increase of arriving patients or when variable specialists are needed for certain treatments. When the emergency department becomes crowded they attempt to transfer additional hospital staff. However, this is not always possible. At such times specialists arrive to treat one specific patient and leave again.

Regarding the capacity of treatment rooms, the interviewees indicate that there is a lack of rooms. Sometimes ambulance arrivals have to wait until there are rooms available at the emergency department. However, the interviewees also indicate that patients spend a long time waiting at the treatment rooms. When it is possible to reduce these waiting times, then the throughput times of patients will decrease.

Relation between number of arrivals and throughput times

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17 number of days within a certain range of arrivals, and gives the medians and percentiles of the corresponding throughput times. The medians and percentiles increase when the number of incoming patients increases. Mostly the differences between groups are small increases. However, comparing the median of the group with the least number of patients and the group with the most number of patients results in a difference of 22.5 minutes. When analyzed in more detail between the separated groups, the differences are not so very great. For example, the difference in the median between the group of 50-60s and the group of 60-70s arrivals is only 2 minutes. Thus the numbers of patients arriving at the emergency department does have a small impact on throughput times. However, this is not significant. Therefore other variables should have a greater effect on throughput times.

Number of patients 0-50 50-60 60-70 70-80 80-90 90-100 Number of days 5 36 67 50 20 5 Median 01:53:00 02:04:00 02:06:00 02:12:00 02:15:00 02:15:30 Percentile 0,1 00:41:00 00:42:00 00:44:00 00:50:00 00:48:00 00:46:00 Percentile 0,3 01:25:00 01:29:00 01:32:00 01:39:00 01:38:30 01:39:06 Percentile 0,7 02:28:00 02:35:00 02:40:00 02:51:00 02:52:00 02:58:00 Percentile 0,9 03:26:00 03:34:00 03:43:00 03:51:00 03:56:00 04:11:36 Table 1 patient arrivals and throughput times

Variability in throughput times

The numbers of patients arriving is variable over the course of a week. A table with the numbers of patient arrivals and the medians and percentiles of the throughput times for different days of the week can be found in Appendix A. Friday is the day with the most arrivals, followed by Monday, while during weekends less patients arrive at the emergency department. However, the numbers of arrivals and the medians during the week are close to each other. Because of these smaller differences in throughput times throughout the different days of the week, the overall daily pattern of patient arrivals can be applied.

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18 median of 02:06:00. This increases until 02:19:00 during the lunch break, and at the end of the day the throughput times become stable again at 02:10:00. During the peak moments of patient arrival the median and the percentiles of throughput times are significantly longer. Therefore, an increase in the work in progress leads to an increase of throughput times during the day.

Daily pattern

In Figure 1 the daily pattern of patients arriving and leaving the emergency department is shown. Before compiling this graph some data correction had been done. Whenever arrival or departure times were missing in the dataset, they were corrected by searching for the information in the medical dossiers. For 26 patients corrections were made from the dataset. Furthermore, the registration of 7 patients were not complete and no data was available in the medical dossiers. These patients were therefore removed from the dataset.

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19 Figure 1 patient arrivals and departures

In Figure 2 another input-output model is shown with patient arrivals and departures. This includes the data of 4095 patients, and from these more data was available on departure times. In addition to the arrival pattern, information about the times that patients were ready to leave the emergency department and the actual departures is included. Compared to Figure 1 the same pattern is shown, with the work in progress increasing until 14:00h, where the input crosses the output. Furthermore, at certain times there is more time available between being ready for departure and actual departure. This indicates that the emergency department experiences a departure delay. This is shown between 12:00h-13:00h, and between 18:00h-20:00h. When departures should increase, between 12:00h-13:00h, then the departure line will cross the arrival line sooner. This results in the emergency department being able to handle patient arrivals sooner and a corresponding decrease in throughput times. 0 1 2 3 4 5 6 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 P atients Time

Patient arrivals and departures

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20 Figure 2 Patient arrivals, ready for departure and departure

Throughput diagram

From the dataset multiple days are analyzed to determine whether the same outcomes were found as during previous research. The diagrams show us that the emergency department is still late in responding to the increase in arrivals. This becomes clear when many patients are included in a diagram. Consequently, the day with the highest number of arrivals is shown in Figure 3. This was on 11-9-2015. On this day 100 patients arrived - the highest number of arrivals. The median of the throughput time for this day is 02:23:30, which is higher than the median of the average median of all data, which was 02:09:00.

On the horizontal axes the hours of the day are shown, while the numbers of patients are shown on the vertical axes. The different lines in the graph represent arrival times, triage times, start of treatment times, and departure times. The arrivals began to increase at around 9:00h. This increase was held at a stable level until 14.00h, when there was a temporary halt in arrivals. From that moment the arrivals continued until 17:00h at the same rate as in the morning, and from 17:00h-21:00h there was a smaller increase of arrivals. The departures also represent a stable line in the beginning. However from 12:00h the increase of the departures decreases. This results in more time between the arrival and the departure, which in turn results in longer throughput times during the day. The higher work in progress leads to higher throughput times. 0 50 100 150 200 250 300 350 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 03:00 p atients time

Patient arrivals, ready for departure and departure

arrival

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21 Figure 3 throughput diagram

In Figure 4 the throughput diagram from 9:00h – 15:00h is shown. From 9:00h, when arrivals begin to increase, the departure line is almost parallel with the arrival rate. However, from 12:00h the departure rate is less steeper than before, this indicates an increase in throughput times. During almost an hour there are no departures while arrivals continue. As can be seen in Figure 4, from 12:00h the time between arrivals and start of treatment became longer. The departure delays result in longer waiting times for patients before they can be treated. This was caused by the earlier arrivals occupying the rooms, resulting in new patients having to wait longer before they could be treated.

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22 Figure 4 throughput diagram

From the analysis of the throughput diagram it can be seen that a delay in patient departures can lead to difficulties in attending to newly arrived patients. As can be seen from Figure 3, there is less waiting time during the mornings. There is almost no space between the arrival of patients and the commencement of treatments. However, there are changes during the day; from 12:00h the time between arrivals and the start of treatment is increasing. Between 12:00h and 13:00h there is a stop in departures which result in longer throughput times. This indicates that the delay in departure influences throughput times, which result in waiting times. The diagrams shows that the possibility of decreasing throughput times by an increase in responsiveness in the morning cannot be made at the start of the process. Possibilities to decrease throughput times are in the last stages of the process.

WIP

In Figure 5 the Work in Progress (WIP) of the day with the highest number of patients is shown, 11-9-2015. The work in progress is defined as the time between the start of the treatment and the departure time. The vertical axes represent the cumulative numbers of patients, while the horizontal axes represents the times of the day. The blue graph shows the work in progress, and the numbers of patients present at the emergency department at a specific time. The green line indicates the number of standard treatment rooms, while the yellow line represents the total number of treatment rooms. This includes special treatment rooms, for example the trauma rooms and the family room. Furthermore, the red line represents all rooms in which

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23 treatments take place at the emergency department, including the triage room and the plaster room.

Figure 5 WIP

As can be seen from Figure 5Figure 5 the maximum number of patients being treated is 17, at these moments all treatment rooms are filled. As can be seen from the graph, the standard treatment rooms are almost all used between 12:00h and 23:00h and between 14:00h and 22:00h. The special rooms are also used several times. Between 14:00h-15:00h and 20:00h-21:00h there are multiple peaks above the treatment room line. This is when patients are treated in a room, and then asked to return to the waiting room. These patients do not need continuous observation and wait mainly for the availability of the x-ray, the plaster room or results from the laboratory. When results or space becomes available, the patients return to a treatment room. In this way non-acute patients do not occupy a room, leaving more rooms for new arrivals. The graph clearly shows the increase of the work in progress during the day and the importance of reducing the throughput times. Therefore improving the treatment process of patients is needed in order to free up resources for new arrivals.

0 2 4 6 8 10 12 14 16 18 20 P atients time WIP 11-9-2015 WIP treatment rooms all rooms

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24 5.2 Improving the treatment process

It can be seen from the graphs already depicted that greater responsiveness during the mornings is required. Productivity should increase, which should lead to a decrease in patient throughput times. Possibilities for improving the process are discussed below.

Attending to new arrivals

From observation it is clear that the emergency department is attends speedily to new arrivals. Most staff immediately attend to patients when they arrive. The interviewees have also confirmed this; one has said that staff are able to initiate proceedings in just 15 minutes. Another interviewee has said that the final phase can take a long time. This is due to paperwork, the verification of medicines, pick-ups from departments or ambulance transfers. On the one hand such things cannot be avoided or improved upon, but on the other physicians can try to increase the speed of this process. This can either be done by attending sooner to the admission of patients, or by motivating other departments to increase their efficiency in the whole process.

Furthermore, an interviewee has stated that since the introduction of the emergency physician, attendance to new arrivals has speeded up. The emergency physician only treats patients at the emergency department. They are thus always present and are familiar with the ways of the department. This has increased the attendance efficiency of new arrivals, compared with the situation without emergency physicians.

In addition to observation and interviews, the data also determines the speed of the initial attendance of patients. The throughput diagram shows less time between arrival and the start of the morning treatments. The diagram also shows that waiting times occur later in the day, probably caused by capacity, which is not available for new arrivals.

Time between arrival and triage

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25 majority of them can therefore are seen within 10 minutes. However, about 25% did not receive their triage code in good time. This can be caused by many patients arriving at the same time, all of whom requiring a triage code, or when the triage nurse also treats other patients. The triage nurse is thus not always immediately available to triage new arrivals. The majority of patients receive their triage code on time, resulting in staff responding quickly to new arrivals.

Arrival time – triage Amount of patients Percentage Cumulative percentage 0-5 1101 54,3% 54,3% 5-10 411 20,3% 74,5% 10-15 241 11,9% 86,4% 15-20 138 6,8% 93,2% 20-25 66 3,3% 96,5% >25 72 3,5% 100,0%

Table 2 arrival time – triage

Waiting time

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Minutes Number of patients Percentages Cumulative percentages 0-5 981 17,2% 17,2% 5-15 2592 45,5% 62,7% 15-30 1143 20,1% 82,8% 30-60 525 9,2% 92,0% 60-120 352 6,2% 98,2% >120 103 1,8% 100,0%

Table 3 waiting times

5.3 Intervention results

In this part of the chapter the results of the intervention will be given. During one week, physicians and nurses were being prompted when two hours had passed since patient treatment had started. At the central board, with all patient information remaining at the emergency department, a pink ‘post-it’ was displayed when patients remained longer than two hours. Staff were prompted to increase their responsiveness when patients stayed longer. Furthermore, the main reasons why patients remained longer than two hours was collected. Impact

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Reasons for delay during the intervention

In Figure 6 the results from the reasons of delay during the intervention are shown. The horizontal axes represent the numbers of patients with specific reasons for the delays, and the vertical axes show the different reasons. There were 50 reasons for delays recorded, and sometimes one patient had multiple reasons for the delay. These are both mentioned in this figure. The most important reason for delays were admissions, followed by consultation with a specialist, and complex patients. Complex patients means patients in need of subsequent research, or when the condition of the patient has worsened during the stay without a clear reason. Other reasons for delays include a patient who did not wish to be hospitalized in the MCL, or a patient who was observed only for a couple of hours.

Figure 6 reasons for delay

Multiple reasons for delays cannot be avoided, because these always take a certain time. For example the complex patients, echo or consultation. Only small improvements can be made by the emergency department itself; for example by trying to reserve an appointment for the echo earlier. This will decrease throughput times. However, it will not result in any major impact. The most frequent reason for delays during the intervention were admissions, which is the final point in the processing of a patient. This indicated that patients were delayed at

16 7 9 8 3 7 0 2 4 6 8 10 12 14 16 18 admission echo consultation complex crowding other patients re ason s

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28 the end of their treatment. Therefore an analysis will be made between the time when patients are ready for departure and actual departure.

Readiness for departure

The time between patients being ready for departure and the actual departure is shown in Table 4. The median is 22 minutes and, concerning the percentiles, about 30% of patients have to wait longer than 31 minutes. The median of all throughput times is 2 hours and 9 minutes. When a patient has to wait half an hour, almost a quarter of that time is spent waiting for the departure rather than being treated. Furthermore, these patients occupy a room, while new arrivals have to wait for rooms to become available. The interviewees claimed that the reason for this delay is caused by the pick-up from the nursing wards. During lunchtime and handovers this can take around one hour. However, there is no difference between the different time intervals during the day, as can be seen in the table of different daytime intervals in Appendix A. This results in not only the pick-up of patients leading to these longer times, but other variables are also an influence. Improvements are possible for this time-lag, leading to possible reductions in throughput times.

Median 00:22:00

Percentile 0,1 00:05:00

Percentile 0,3 00:14:00

Percentile 0,7 00:31:00

Percentile 0,9 00:51:00

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6. Discussion and conclusion

The preliminary research found that during the mornings the emergency department did not respond adequately to increased arrivals (Van Achteren, 2014). According to Van Achteren (2014) responsiveness during the morning should increase the productivity of the staff, which should in turn lead to lower throughput times during the day. During the course of this study the impact of responsiveness on throughput times was researched.

Seen from the perspective of the data analysis, improvements were possible during the mornings. Patient arrivals increased at a greater rate than departures. Firstly, the available capacity has been researched, giving no indication that one of the resources had high structural problems. However, some of the available resources were not working productively enough. The nurses and physicians seemed to be the bottleneck in the process, and increasing their productivity is the key in decreasing throughput times at the emergency department. Resulting from multiple observation days, and from the interviews, it became clear that during the mornings the staff attended to new patients as soon as possible. Furthermore, the data analysis of waiting times and throughput diagrams also indicated that patients are attended promptly in the mornings. This results in delays in the treatment process occurring later in the overall process. Improvements in the productivity of staff is not therefore required at the beginning of the process, but rather during the middle and / or at the end.

An intervention was made to increase the responsiveness at the end of the treatment process of a patient. By improving this part, patients were able to leave the emergency department earlier and therefore more places became available for new arrivals. By having more information concerning the length of the stay of patients, they were prompted to hasten their treatment and thus to release patients sooner. The result of the intervention was a heightened awareness in the staff. Previously, various employees had not been enabled to focus on the time spent with their patients, and this had changed significantly. This awareness can be seen as an indication that staff could be working more productively than they had previously been doing. Improvements in responsiveness are certainly possible at the end of the treatment process.

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30 the data, this time can be very long, and this has an immediate and major large effect on the throughput times of patients. Not only the patients waiting before they are able to leave have an increase in throughput times, but new arrivals are also affected. The patients occupy resources and staff, as well as treatment rooms, and this results in the new arrivals having to wait before these resources become available again. Reducing this time should lead to earlier departures, less work in progress and a reduction in throughput times.

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7. Recommendations

From this study it is clear that the capacity within the emergency department can be used more effectively. The attendance rate during the morning appears to be adequately prompt. However, there is still room for improvement at the final part of the process, and solutions here are certainly possible. This is especially the case between those times when patients become ready for departure and their actual departure. Improvements are also possible before they are ready to leave. By analyzing staff working habits opportunities can be found for improvement in the process. More detailed information is required concerning staff working patterns at this stage of the process to enable improvement possibilities. Increasing awareness of the length of stays can also be used to increase the efficiency of the process. When staff are more aware of the length of stays of patients, they are prompted to work with greater efficiency.

An opportunity to deal with the high variability of the required capacity would be to use flexible capacity at the emergency department. By increasing collaboration with other departments, a greater sharing of resources can be achieved. Examples would be to work more with the intensive care or recovery departments, because they also have to deal with great variability. It would be helpful for different departments to help each other when necessary. During busy times physicians and nurses from these departments could join the staff at the emergency department, while during quieter moments physicians and nurses could help other departments also.

A particularly time-consuming part of the treatment process takes place in the laboratory, with blood tests invariably taking about an hour. By increasing laboratory efficiency the processes at the emergency department would be positively affected. For example, coming to agreements with the laboratory concerning the time that results become available would be helpful.

7.1 Recommendations for further research

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32 It would also be of great relevance and interest to discover whether improvements would be possible in laboratory efficiency. By discovering delays throughout the process, results can be made available sooner, decreasing the throughput times of patients at the emergency department.

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8. References

Asplin, B.R., Magid, D.J., Rhodes, K.V., Solberg, L.I., Lurie, N., Camargo, C.A. (2003). A conceptual model of emergency department crowding. Annals of emergency medicine, 42(2), 173-180.

Chan, L., Reilly, K.M., Salluzzo, R.F. (1997). Variables that affect patient throughput times in an academic emergency department. American journal of medical quality, 12(4), 183-186.

Derlet, R.W., Richards, J.R. (2000). Overcrowding in the Nation’s Emergency Departments: complex causes and disturbing effects. Annals of emergency medicine, 35 (1).

Derlet, R.W., Richards, J.R., Kravitz, R.L. (2001). Clinical practice frequent overcrowding in U.S. Emergency Departments. Academic emergency medicine, 8 (2).

Dijk, T. (2013). How can the throughput time of patients in the emergency department be improved? University of Groningen.

Ekelund, U., Kurland, L., Eklund, F., Torkki, P., Letterstal, A., Lindmarker, P., Castrén, M. (2011). Patient throughput times and inflow patterns in Swedisch emergency departments. Scandinavian journal of trauma, resuscitation and emergency medicine, 19 (37).

Ghosh, A., Das, S., Deshpande, A. (2014). Effect of responsiveness and process integration in supply chain coordination. IUP Journal of Supply chain management, 11 (1), 7-17.

Guyatt G.H., Kirshner B., Jaeschke R. (1992). Measuring health status: What are the necessary measurement properties? J Clin Epidemiol, 45, 1341-1345.

Hopp, W.J., Spearman, M.L. (2008). Supply chain science. Long grove; Waveland Press. Jones, S.S., Alun, T., Evans, R.S., Welch, S.J., Haug, P.J., Snow. G.L. (2008). Forecasting daily patient volumes in the emergency department. Academic emergency medicine, 15 (2), 159-170.

Karlsson, C. (2009). Researching Operations Management. New York.

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34 Lane, D.C., Monefeldt, C., Rosenhead, J.V. (2000). Looking in the wrong place for healthcare improvements; A system dynamics study of an accident and emergency department. Journal of the Operational Research Society, 51, 518-531.

McCarthy, M.L., Zeger, S.L., Ding, R., Levin, S.R., Desmond, J.S., Lee, J., Aronsky, D. (2009). Crowding delays treatment and lengthens emergency department length of stay, even among high-acuity patients. Annals of emergency medicine, 54 (4), 492-503.

McCaughey, D., Erwin, C.O., DelliFraine J.L., McVey, L. (2015). Improving Capacity Management in the Emergency Department: A Review of the Literature, 2000-2012. Journal of Healthcare Management, 60(1).

MCL. (2014) Jaarverslag. Accessed on October 23th, 2015 at https://www.mcl.nl/over-het-mcl/jaarverslag.

Meredith, J. (1998). Building operations management theory through case and field research. Journal of operations management, 16, 441-454.

Pines, J.P., Batt, R.J., Hilton, J.A., Terwiesch, C. (2011). The financial consequences of lost demand and reducing boarding in hospital emergency departments. Annals of emergency medicine, 58 (4), 331-340.

Roukema, J., Steyerberg, E.W., Van Meurs, A., Ruige, M., Van der Lei, J., Moll, H.A. (2006). Validity of the manchester triage system in paediatric emergency care. Journal of emergency medicine, 23 (12).

Smith, D.M., Martin, D.K., Langefeld, C.D., Miller, M.E., Freedman, J.A. (1995). Primary care physician productivity. Journal of general internal medicine, 10 (9), 495-503.

Soepenberg, G. D., Land, M. J., & Gaalman, G. J. C. (2012). A framework for diagnosing the delivery reliability performance of make-to-order companies.International Journal of Production Research, 50(19), 5491–5507.

Solberg, L.I., Asplin, B.R., Weinick, R.M., Magid, D.J. (2003). Emergency department

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35 Van Achteren, G. (2014). Care for waiting: the effect of emergency department responsiveness on patient waiting times. University of Groningen.

Van der Linden, C., Reijnen, R., Derlet R.W., Lindeboom, R., Van der Linden, N., Lucas C., Richards, J.R. (2013). Emergency department crowding in The Netherlands: manager’s experiences. International Journal of emergency medicine, 6 (41).

Van der Vaart, T., Vastag, G., Wijngaard, J. (2011). Facets of operational performance in an emergency room (ER). International Journal of Production Economics, 133, 201-211.

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9. Appendix A: Tables

Specialism Number of patients Percentage

Surgery 4347 34,8% Cardiology 2350 18,8% Internal 2113 16,9% Neurology 1061 8,5% Lung 878 7,0% Orthopedics 638 5,1% Other 1112 8,9%

Table 5 patients assigned to a specialism

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Times Median Percentile 0,1 Percentile 0,3 Percentile 0,7 Percentile 0,9 Day 02:09:00 00:47:00 01:35:00 02:44:00 03:46:00 7-13 02:15:00 00:48:00 01:39:00 02:53:00 04:01:00 7-10 02:06:00 00:44:00 01:28:00 02:40:00 03:44:42 10-13 02:19:00 00:50:00 01:44:00 02:59:00 04:08:00 13-18 02:13:00 00:46:00 01:36:00 02:49:00 03:49:00 13-15 02:16:00 00:44:00 01:38:00 02:51:42 03:48:54 15-18 02:10:00 00:47:00 01:35:00 02:47:00 03:49:48 Table 7 Throughput times during the day

Times Median Percentile 0,1 Percentile 0,3 Percentile 0,7 Percentile 0,9

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10. Appendix B: Coding interviews

The interviews are done within the emergency department of the MCL. One interview was done with an emergency physician, another interview was done with a care coordinator (ZoCo). The interviews with codes are shown in appendix B.

To analyze the results of the interviews, the results were coded. The codes are divided as follows;

- Impact intervention --

- Reason delay --

- Capacity --

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11. Appendix C: Interviews Interview Arts

Op sommige momenten kan het heel druk zijn op de eerste hulp, werk je dan ook echt op een andere manier of ervaren jullie dat ook anders?

Ik denk dat op drukke momenten dat de druk op ons ook hoger is en dat we het tempo denk ik wel moeten opvoeren. ’s Morgens bijvoorbeeld als het begint dan doen wij ook vaak wat rustiger aan, en als het druk is probeer je de capaciteit ook voldoende te krijgen; dat je voldoende mensen hebt, voor ons geldt voldoende dokters in ieder geval, en als je denkt dat het tekort is dan ga je rondbellen waar je dan nog eventueel personeel vandaan kunt halen. En dat doe je natuurlijk niet als het rustig is.

Lukt het dan ook altijd om extra mensen erbij te krijgen?

Nee dat lukt niet altijd, soms is de bezetting op de afdeling het, want dan gaat het met name om dat we dokters van de afdeling halen, en dat daar de bezetting ook heel krap is, nou dan kunnen zij geen extra dokter leveren. Dus dan lukt het niet maar soms kan het ook wel, dan komen ze tussendoor toch even 1 patient beoordelen. En dat is het personeel hier naartoe halen, wat we soms nog wel doen is dat we patienten proberen sneller door te sturen, en dat doen we natuurlijk ook alleen als het druk is. Als de druk hier heel hoog is of we plekken en als ambulances heel lang moeten wachten proberen we sommige patienten direct door te laten gaan naar de afdeling. En dat lukt ook wel, tenminste met sommige patientencategorieën lukt dat wel aardig.

En als je dan kijkt naar de hele week, is het dan ook echt elke dag heel druk of valt dat best wel mee?

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40

Hebben jullie ook echt een groot verschil tussen de ochtend en de middag, of niet?

Ja het begin van de ochtend wel, in het begin van de ochtend is het vaak rustiger, soms begint het ook wel met allemaal instabiele patienten ’s morgens, maar meestal is het ’s morgens het eerste uur, de eerste twee uur is het wat rustig, daarna begint het te lopen, dan krijg je de aanmeldingen eerst en als ze komen, dan komen ze natuurlijk allemaal binnen, en ’s middags is het veel drukker over het algemeen, dan loopt het ook de hele tijd door; de drukste momenten zijn van 11-8, 11 uur ’s ochtends tot 8 uur ’s avonds. ’s Avonds is dat ook verschillend, begin van de avond is het drukker, dan aan het einde van de avond.

En hebben jullie ook het idee dat er voldoende kamers beschikbaar zijn?

Nee. We hebben soms wel, we hebben af en toe wel dat ambulances moeten wachten in de gang, en daar is iedereen ook van op de hoogte hor, maar dan geven we ook aan van; we zijn bezig met plekken te creeëren, maar dat lukt niet en dan heb je gewoon tekort. Dan proberen we iemand ook wel eens niet direct door te sturen wat ik net zei naar de afdeling maar wel zo snel mogelijk dan wel om ze direct door te sturen zodat je wel een plekje creeert. Maar je hebt eigenlijk wel meer kamers nodig.

En wat betreft verpleegkundigen, zijn die er wel altijd voldoende?

Sinds de uitbreiding zijn die er wel voldoende denk ik. Een enkele keer heb je wel als je allemaal instabiele patienten tegelijk hebt, je wil graag 2 verpleegkundigen per instabiele patient, dat het dan wel wat krap is, want dan ben je ze dus zomaar kwijt, maar over het algemeen, is er nog niet zo heel lang geleden een uitbreiding geweest, ’s morgens beginnen ze met minder, half 10 komen er weer een paar bij, en geloof ik om 1 uur komen er weer 2 bij, en zodat je ’s middags dus ook wel voldoende hebt.

Hoe kiezen jullie wat jullie eerste taken zijn, waar jullie mee bezig gaan zeg maar?

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41 lang dus dat zet je meteen in. Daarna ga je kijken welke dokter er naartoe gaat, of je doet het zelf of dan kan het ook zo zijn dat de coördinator van de spoed die stuurt meteen een dokter naar de overdracht toe van de ambulance, die zet de patiënt dan meteen in gang, dat hangt ervan af hoe ziek iemand is. Dan laat je de verpleegkundige heel even hun taak uitvoeren, en dan kun jij in de tussentijd voorgeschiedenis opzoeken, want dan heb je ook wat achtergrondinformatie, meteen daarna kun je, dan ga je, ik in principe wel anamnese doen van het lichaam, onderzoek en dan is het vaak toch even wachten op het lab, daar ben je geen uur mee bezig. Dan wacht je af, in de tussentijd kun je nog nadenken van zijn er nog meer onderzoeken, maar eigenlijk wel zo snel mogelijk, en ik probeer dat ook altijd al tijdens de anamnese, als de verpleegkundige erbij is, dan kom ik nog ergens achter, want de overdracht van de ambulance is natuurlijk korter dan de anamnese; en dan zeg ik meteen van wil je dat ook meteen nog even laten bepalen in het bloed, en dan wordt het meteen uitgevoerd. Urine is ook wel eens lastig, maar dat komt meer omdat patienten niet altijd kunnen plassen.

En hebben jullie ook een beetje van wie het eerst komt die het eerst maalt, of is dat eigenlijk helemaal niet zo? (bij patienten)

Nee, dat is triage, de ernst van de ziekte. Nee, als ze allemaal even ziek zijn, dan wel, maar als iemand veel ernstiger, volgens mij hangt er ook ergens in de wachtkamer een formulier voor patienten die in de wachtkamer zitten; het kan zijn dat u al heel lang wacht en dat er iemand anders eerder geholpen wordt, maar dat komt gewoon door triage, dus dat iemand anders meer behoefte heeft. Nee, kijk een reanimatie gaat natuurlijk voor een iemand met een gebroken been, hangt ervan af waar het been gebroken is, maar een teen ofzoiets dan gaat een reanimatie gewoon voor. Dus dat is niet op volgorde van wat er binnenkomt. Dan moet diegene met zn teen gewoon even langer wachten.

En kijken jullie verder dan ook echt naar de triagetijden ook tijdens de behandeling zelf, of niet?

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42 Dat is een goede vraag, volgens mij verandert die kleur niet, die blijft precies hetzelfde. Ja want dan is er al iemand langs geweest, dus die weet dan ook; er hoeft niet iemand binnen 10 minuten een dokter, want die is er al, dus het heeft geen consequenties, denk ik. De kleur blijft volgens mij gewoon hetzelfde.

Wat hebben jullie gemerkt van de interventie?

Bewustwording. Dat je in ieder geval bewust bent van dat je ziet dat een patient, dat dat echt visueel duidelijk wordt, dat een patient langer dan 2 uur ligt. Want anders dan weten we dat niet precies, dan denken we op gegeven moment wel van; he, die ligt er misschien al een tijdje, en dan ga je, dan ga ik er wel achteraan als iemand langer ligt, dan ga ik naar de dokter toe, de artsassistent die erover gaat en vraag ik van weet je hoe lang het nog duurt, waar ben je mee bezig en kan ik ook helpen ergens mee, of dat we het proces een beetje bevorderen, maar je houdt die tijd niet in de gaten, doordat je zelf ook heel druk bezig bent, je hebt geen flauw idee hoe lang het dan is, en als je die plakkertjes erop plakt, en iemand is langer dan 2 uur , dan weet je; oh dat is 2 uur; dan ga ik er nu wat mee doen. Ik weet niet of daardoor de tijd echt korter is geweest ofzo, dat weet ik niet hor.

Bewustwording is ook al iets

Ja dat sowieso wel, dat je wel denkt he die ligt er 2 uur, daar ga ik dan nu achteraan en anders denk je van die ligt er wel even, maar hoe lang, misschien ben ik zelf een beetje ongeduldig, en is het nog maar een uur, je denkt ik wacht nog maar even.

Misschien dat er in de toekomst met het elektronisch scherm dit aangegeven kan worden?

Ja dat er een vlaggetje aankomt ofzo

Of dat het rood gaat flikkeren

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43

Soms kan het gewoon niet anders

Soms is dat ook zo hor, ja als je een gecompliceerde patient hebt, of er komt weer iets bij, of er komt iets tussendoor of

Wat denk je zelf wat de meeste tijd inneemt bij een behandeling?

Losstaande van wachten?

Ja

Soms kan het zijn dat ze ’s middags best wel lang moeten wachten.

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Als je kijkt naar een gemiddeld proces van een patient, van begin naar het midden en dan heb je het eind, waar zitten de beste dingen, waar het sneller zou kunnen, in welk gedeelte?

Aan het einde. De afrondende fase. Het begin denk ik niet, we zijn best wel snel erbij en zeker sinds de spoedartsen er zijn, daar zijn we ook wel heel snel bij en de onderzoeken proberen we zo snel mogelijk aan te vragen, maar de afrondende fase dat is vaak nog best wel lang. Papierwerk bij opnames, medicatie, medicatie verificatie, medicatie nog een keer verifieren, nog een keer verifieren, en dan aconeren, ja maar dat soort dingen, papierwerk zit heel veel tijd in ook hor, en dat is wel de afrondende fase. Als iemand naar huis gaat scheelt dat wel weer want dat is veel minder werk, maar. Dat er al lang duidelijk is wat er met de patient gaat gebeuren maar dat dat allemaal nog echt op gang gebracht moet worden. En het ophalen van een patient ook wel volgens mij. Als wij alles rond hebben,

Ook als een patient met een ambulance weggat,

Of ook naar verpleeghuizen of zoiets, dat duurt altijd heel lang. Ja maar dat hebben we gelukkig niet zo vaak. Als de patient niet zelf naar huis kan en die moet inderdaad ergens naartoe gebracht worden door de ambu, ja dat is heel lang. Die ambu staat ook vast en die moet ingeroosterd worden. En soms het ophalen, vooral tussen de middag. Als het lunchpauze is op veel afdelingen, dan is de helft van de afdeling is dan aan het eten, en de rest kan niet allemaal weg want dan is er niemand meer op de afdeling, dus die moet wachten tot mensen van de lunch terug zijn. Ja en daar zitten wij dan lekker met die patienten en dat is soms ook wel heel lang hor. Het kan soms wel een uur duren, ja dat vind ik lang.

En overplaatsingen, dat is ook met de ambu, maar dat hebben we nu ook met volmeldingen; als we volmelding hebben dan kunnen we de patient hier wel beoordelen maar niet opnemen want we kunnen de patient niet kwijt, die moet ook overgeplaatst worden, dat duurt ook lang. Want dan moet je een ambu regelen en de patient moet ergens anders heen.. met name die wachttijd.

Interview ZoCo

Wat is voor jullie het grootste verschil tussen een druk en een rustig moment?

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45 alerter als het druk is, dat merk ik dan ook wel, dan vergt het gewoon meer sturing. Als het rustig is dan loopt het eigenlijk wel een beetje vanzelf wel.

En heeft u het gevoel dat er altijd voldoende verpleegkundigen aanwezig zijn?

Mwah, het is wel beter geworden, maar veel patienten moeten met zn tweeën opgevangen worden, bijvoorbeeld de trauma’s, trombolyses en sepses en de zieke kinderen, er zijn een aantal patienten die met twee verpleegkundigen opgevangen moeten worden, en soms is iedereen bezig, dus dan ben je al blij dat je 1 verpleegkundige erbij hebt, dan mis je wel eens een verpleegkundige, ja.

En is dit ook vaker in de ochtend of in de middag, of is daar geen verschil tussen?

Ja daar is wel verschil tussen, meestal begint het rond een uur of 10/11 soms en dan tijdens de lunch is het eigenlijk altijd wel druk en hoe lang dat aanhoudt, dat verschilt, soms duurt dat echt tot 8 uur ’s avonds en soms is het om 5 uur/ 6 uur rustig, maar dat zijn wel de piekmomenten, ja.

En heb je ook het gevoel dat er voldoende kamers zijn?

Nee, wij hebben wel ruimtegebrek, ja, duidelijk.

En bij meer kamers zouden er dan voldoende artsen zijn, of zouden die er dan ook meer bij moeten komen?

Ja daar moet je wel je formatie op aanpassen.

Hoe kiezen jullie uit welke patient als eerste in de behandelkamer mag komen?

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En ook als zeg maar de patient wel met allemaal dezelfde kleur in de wachtkamer zouden zitten, hoe kiezen jullie het dan verder uit?

Kijken jullie dan eventueel naar de specifieke klachten?

Ja ik weet zo snel even geen voorbeeld te noemen en naar de kliniek, hoe ziek is iemand daarbij. En soms heb je al lab, dan kun je aan de hand van labuitslagen al kijken van goh, hoe ziek is iemand echt. Want dat triage, dat doet 1 verpleegkundige en als je dan lab hebt, dan kun je triage ook meer onderbouwen, als iemand zich echt heeel ziek voelt, of bijvoorbeeld hoge koorts heeft, ja dan snap je wel dat diegene zich een beetje ellendig voelt en eerder gezien moet worden dan iemand anders die zich nog wel redelijk voelt, met dezelfde klachten.

En doen jullie ook een beetje van wie het eerst komt, die het eerst maalt?

Als ze dezelfde kleurcodering hebben wel, ja, dan gaat het ook wel op tijd.

En hoe kiezen jullie verder dan de kamers uit, waar ze in gaan, acuut gaat zeker in de traumakamers, en verder?

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Wat hebben jullie gemerkt van de interventie?

Het valt natuurlijk gelijk op dat daar zo’n plakkertje op zit en dan denk je wel van; he die ligt hier ook alweer 2 uren, dus je bent je er bewuster van dat het langer duurt, door zo’n plakkertje erop, maar ik weet niet of het echt effect heeft op de doorlooptijd, dat het daardoor dan ook ineens sneller gaat. Dat weet ik niet.

Enkel bewustwording, verder is er niet echt iets veranderd?

Misschien dat je eens een keer sneller naar de arts gaat om te vragen van he, waar wachten we op, dat soort dingen.

En als je kijkt naar het proces van een patient, welk gedeelte zou je dan denken dat de meeste tijd inneemt?

Het wachten op het lab, dat duurt sowieso een uur, want onze handelingen, dat is echt in een kwartier kunnen wij daar klaar mee zijn, dus dat is het niet. En misschien het overleg van de arts met de specialist, zij kunnen ook niet altijd direct bellen met de specialist, dus soms moeten ze bijvoorbeeld wachten tot ze terug gebeld worden of dat de specialist langs komt, dus daarop denk ik wel. En als een patient bijvoorbeeld voor echo moet dan moeten we daar ook wel eens lang op wachten omdat er niet gelijk plek is; dus aanvullende onderzoeken en het lab. Dat is echt een dingetje.

En als je nog kijkt naar een hele behandeling, dan heb je een begin, het midden en het eind, welk gedeelte zouden jullie dan denken, waar het sneller zou kunnen?

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The influence of emergency department responsiveness on throughput times.

The influence of emergency department

responsiveness on throughput times.

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